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https://github.com/vale981/ray
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57544b1ff9
- This PR completes any previously missing PyTorch Model counterparts to TFModels in examples/models. - It also makes sure, all example scripts in the rllib/examples folder are tested for both frameworks and learn the given task (this is often currently not checked) using a --as-test flag in connection with a --stop-reward.
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
"""Simple example of setting up a multi-agent policy mapping.
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Control the number of agents and policies via --num-agents and --num-policies.
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This works with hundreds of agents and policies, but note that initializing
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many TF policies will take some time.
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Also, TF evals might slow down with large numbers of policies. To debug TF
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execution, set the TF_TIMELINE_DIR environment variable.
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"""
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import argparse
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import gym
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import random
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import ray
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from ray import tune
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from ray.rllib.examples.env.multi_agent import MultiAgentCartPole
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from ray.rllib.examples.models.shared_weights_model import \
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SharedWeightsModel1, SharedWeightsModel2, TorchSharedWeightsModel
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from ray.rllib.models import ModelCatalog
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from ray.rllib.utils.framework import try_import_tf
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from ray.rllib.utils.test_utils import check_learning_achieved
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tf = try_import_tf()
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parser = argparse.ArgumentParser()
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parser.add_argument("--num-agents", type=int, default=4)
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parser.add_argument("--num-policies", type=int, default=2)
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parser.add_argument("--stop-iters", type=int, default=20)
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parser.add_argument("--stop-reward", type=float, default=150)
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parser.add_argument("--stop-timesteps", type=int, default=100000)
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parser.add_argument("--simple", action="store_true")
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parser.add_argument("--num-cpus", type=int, default=0)
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parser.add_argument("--as-test", action="store_true")
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parser.add_argument("--torch", action="store_true")
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init(num_cpus=args.num_cpus or None)
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# Register the models to use.
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mod1 = TorchSharedWeightsModel if args.torch else SharedWeightsModel1
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mod2 = TorchSharedWeightsModel if args.torch else SharedWeightsModel2
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ModelCatalog.register_custom_model("model1", mod1)
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ModelCatalog.register_custom_model("model2", mod2)
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# Get obs- and action Spaces.
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single_env = gym.make("CartPole-v0")
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obs_space = single_env.observation_space
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act_space = single_env.action_space
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# Each policy can have a different configuration (including custom model).
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def gen_policy(i):
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config = {
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"model": {
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"custom_model": ["model1", "model2"][i % 2],
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},
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"gamma": random.choice([0.95, 0.99]),
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}
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return (None, obs_space, act_space, config)
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# Setup PPO with an ensemble of `num_policies` different policies.
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policies = {
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"policy_{}".format(i): gen_policy(i)
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for i in range(args.num_policies)
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}
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policy_ids = list(policies.keys())
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config = {
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"env": MultiAgentCartPole,
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"env_config": {
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"num_agents": args.num_agents,
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},
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"log_level": "DEBUG",
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"simple_optimizer": args.simple,
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"num_sgd_iter": 10,
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"multiagent": {
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"policies": policies,
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"policy_mapping_fn": (lambda agent_id: random.choice(policy_ids)),
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},
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"use_pytorch": args.torch,
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}
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stop = {
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"episode_reward_mean": args.stop_reward,
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"timesteps_total": args.stop_timesteps,
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"training_iteration": args.stop_iters,
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}
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results = tune.run("PPO", stop=stop, config=config)
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if args.as_test:
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check_learning_achieved(results, args.stop_reward)
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ray.shutdown()
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